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PerformanceAnalytics_Class.py
Source:PerformanceAnalytics_Class.py 
1#Python Script---------------------------------------------------------------------------------------------------------------------------2#Title: Performance Analytics3# coding: utf-84#---5import pandas as pd6import datetime 7import time8import math9import csv10import numpy as np11import scipy12from scipy.stats import trim_mean, kurtosis13from scipy.stats.mstats import mode, gmean, hmean14from scipy.stats import norm15from pandas.tseries.offsets import BDay16import ta as ta17class Performance():18 def __init__(self, Reuters):19 self.reuters = Reuters20 21 #actual price data22 url_csv = "http://matterhorn-lab.herokuapp.com/download/"+ str(self.reuters)23 prices_data = pd.read_csv(url_csv, sep = ",") 24 start, stop, step = 0, -14, 125 prices_data["Date"] = prices_data["Date"].str.slice(start, stop, step)26 27 prices_data = prices_data[::-1].reset_index()28 prices_data = prices_data.drop(['index'], axis=1)29 prices_data = prices_data.sort_values(["Date"], ascending = [1]).reset_index()30 prices_data = prices_data.drop(['index'], axis=1)31 32 33 #static34 stock_info = pd.read_csv('data/DB_Stock_Info.csv', sep=';')35 key_ratios = pd.read_csv('data/DB_Stock_Key_Ratios.csv', sep=';')36 37 # get the number of business days38 c_size = len(prices_data.columns)39 r_size = prices_data.shape[0]40 41 date_data = prices_data.iloc[:,0]42 today = date_data.iloc[r_size-1:r_size]43 today = pd.to_datetime(today)44 today = datetime.date(int(today.dt.year),int(today.dt.month),int(today.dt.day))45 46 # calculate days yesterday47 yesterday = today -BDay(1)48 # calculate days last week49 lastweek = today -BDay(5)50 # calculate days since start month51 startmonth = datetime.date(int(today.strftime('%Y')),int(today.strftime('%m')),1)52 days_start_month = np.busday_count(startmonth,today)53 # calculate days last month54 lastmonth = datetime.date(int(today.strftime('%Y')),int(today.strftime('%m'))-1,int(today.strftime('%d')))55 days_last_month = np.busday_count(lastmonth,today)56 # calculate days since start year57 yearstart = datetime.date(int(today.strftime('%Y')),1,1)58 days_start_year = np.busday_count(yearstart,today)59 # calculate days one year60 lastyear = datetime.date(int(today.strftime('%Y'))-1,int(today.strftime('%m')),int(today.strftime('%d')))61 days_last_year = np.busday_count(lastyear,today)62 # calculate days three years63 last3years = datetime.date(int(today.strftime('%Y'))-3,int(today.strftime('%m')),int(today.strftime('%d')))64 days_last_3years = np.busday_count(last3years,today)65 # calculate days five years66 last5years = datetime.date(int(today.strftime('%Y'))-5,int(today.strftime('%m')),int(today.strftime('%d')))67 days_last_5years = np.busday_count(last5years,today)68 # calculate days ten years69 last10years = datetime.date(int(today.strftime('%Y'))-10,int(today.strftime('%m')),int(today.strftime('%d')))70 days_last_10years = np.busday_count(last10years,today)71 72 # calculate returns 73 prices = prices_data.iloc[:,1:c_size]74 #returns = math.log(prices/prices.shift(1))75 #prices_year = prices.iloc[r_size-days_year:r_size]76 price_change = pd.DataFrame(prices.values[r_size-1] - prices)77 price_change.columns = [ Reuters]78 returns = prices.pct_change(1)79 80 # calculate price and return today81 returns_today = returns.iloc[r_size-1:r_size]82 prices_today = prices.iloc[r_size-1:r_size]83 price_change_today = price_change.iloc[r_size-1:r_size]84 85 # calculate price and return yesterday86 returns_yesterday = returns.iloc[r_size-2:r_size]87 prices_yesterday = prices.iloc[r_size-2:r_size]88 cum_return_yesterday = prices_yesterday.loc[r_size-1] / prices_yesterday.loc[r_size-2] -189 average_return_yesterday = np.mean(returns_yesterday)90 price_change_yesterday = price_change.iloc[r_size-2:r_size-1]91 92 # calculate price and return last week93 returns_last_week = returns.iloc[r_size-5:r_size]94 prices_last_week = prices.iloc[r_size-5:r_size]95 cum_return_last_week = prices_last_week.loc[r_size-1] / prices_last_week.loc[r_size-5] -196 average_return_last_week = np.mean(returns_last_week)97 price_change_last_week = price_change.iloc[r_size-5:r_size]98 vola_last_week = np.std(returns_last_week)99 sharpe_ratio_last_week = average_return_last_week /vola_last_week100 101 # calculate price and return since start month102 returns_start_month = returns.iloc[r_size-days_start_month:r_size]103 prices_start_month = prices.iloc[r_size-days_start_month:r_size]104 cum_return_start_month = prices_start_month.loc[r_size-1] / prices_start_month.loc[r_size-days_start_month] -1105 average_return_start_month = np.mean(returns_start_month)106 price_change_start_month = price_change.iloc[r_size-days_start_month:r_size]107 vola_start_month = np.std(returns_start_month)108 sharpe_ratio_start_month = average_return_start_month /vola_start_month109 110 # calculate price and return last month111 returns_last_month = returns.iloc[r_size-days_last_month:r_size]112 prices_last_month = prices.iloc[r_size-days_last_month:r_size]113 cum_return_last_month = prices_last_month.loc[r_size-1] / prices_last_month.loc[r_size-days_last_month] -1114 average_return_last_month = np.mean(returns_last_month)115 price_change_last_month = price_change.iloc[r_size-days_last_month:r_size]116 117 # calculate price and return since start year118 returns_start_year = returns.iloc[r_size-days_start_year:r_size]119 prices_start_year = prices.iloc[r_size-days_start_year:r_size]120 cum_return_start_year = prices_start_year.loc[r_size-1] / prices_start_year.loc[r_size-days_start_year] -1121 average_return_start_year = np.mean(returns_start_year)122 price_change_start_year = price_change.iloc[r_size-days_start_year:r_size]123 vola_start_year = np.std(returns_start_year)124 sharpe_ratio_start_year = average_return_start_year /vola_start_year125 126 # calculate price and return one year127 returns_last_year = returns.iloc[r_size-days_last_year:r_size]128 prices_last_year = prices.iloc[r_size-days_last_year:r_size]129 cum_return_last_year = prices_last_year.loc[r_size-1] / prices_last_year.loc[r_size-days_last_year] -1130 average_return_last_year = np.mean(returns_last_year)131 price_change_last_year = price_change.iloc[r_size-days_last_year:r_size]132 vola_last_year = np.std(returns_last_year)133 sharpe_ratio_last_year = average_return_last_year /vola_last_year134 135 # calculate price and return three years136 returns_last_3years = returns.iloc[r_size-days_last_3years:r_size]137 prices_last_3years = prices.iloc[r_size-days_last_3years:r_size]138 cum_return_last_3years = prices_last_3years.loc[r_size-1] / prices_last_3years.loc[r_size-days_last_3years] -1139 average_return_last_3years = np.mean(returns_last_3years)140 price_change_last_3years = price_change.iloc[r_size-days_last_3years:r_size]141 vola_last_3years = np.std(returns_last_3years)142 sharpe_ratio_last_3years = average_return_last_3years /vola_last_3years143 144 # calculate price and return five years145 returns_last_5years = returns.iloc[r_size-days_last_5years:r_size]146 prices_last_5years = prices.iloc[r_size-days_last_5years:r_size]147 cum_return_last_5years = prices_last_5years.loc[r_size-1] / prices_last_5years.loc[r_size-days_last_5years] -1148 average_return_last_5years = np.mean(returns_last_5years)149 price_change_last_5years = price_change.iloc[r_size-days_last_5years:r_size]150 vola_last_5years = np.std(returns_last_5years)151 sharpe_ratio_last_5years = average_return_last_5years /vola_last_5years152 153 # calculate price and return ten years154 returns_last_10years = returns.iloc[r_size-days_last_10years:r_size]155 prices_last_10years = prices.iloc[r_size-days_last_10years:r_size]156 cum_return_last_10years = prices_last_10years.loc[r_size-1] / prices_last_10years.loc[r_size-days_last_10years] -1157 average_return_last_10years = np.mean(returns_last_10years)158 price_change_last_10years = price_change.iloc[r_size-days_last_10years:r_size]159 vola_last_10years = np.std(returns_last_10years)160 sharpe_ratio_last_10years = average_return_last_10years /vola_last_10years161 162 # all time163 cum_return_all = prices.loc[r_size-1] / prices.loc[3] -1164 average_return_all = np.mean(returns)165 vola_all = np.std(returns)166 sharpe_ratio_all = average_return_all /vola_all167 # year high, low and range168 year_high = prices_last_year.max()169 year_low = prices_last_year.min()170 range_low_high = year_high - year_low171 range_percent = range_low_high / year_high172 173 # investment of 10000 CHF174 help_investment = returns175 help_investment = help_investment.drop(help_investment.index[0:2])176 help_invest = [0] * (c_size-1)177 help_investment.iloc[0] = help_invest178 investment = (1+help_investment).cumprod() *10000179 180 # describtive statistics181 mean = np.mean(returns_last_year)182 std = np.std(returns_last_year)183 Z_99 = norm.ppf([0.01])184 185 186 # Value at risk187 Covar_Var = -(mean-Z_99*std)188 n_sims = 1000000189 SimVar =[]190 for i in range(c_size-1):191 np.random.seed(i)192 random_numbers = np.random.normal(0, 1, n_sims)193 sim_returns=mean[i]+std[i]*random_numbers194 SimVar = (np.percentile(sim_returns, 1))195 196 HistVar=[]197 for i in range(0,r_size-days_last_year):198 help_VaR = returns.iloc[r_size-days_last_year-i:r_size-i]199 HistVar.append(np.percentile(help_VaR, 1))200 201 df_HistVar = {}202 df_HistVar= {"Name": HistVar}203 HistVar = pd.DataFrame(HistVar)204 205 # Expected Shortfall206 cutoff = int(round(days_last_year * 0.01,0))207 208 ES = []209 for i in range(0,r_size-days_last_year):210 help_ES = returns.Price.iloc[r_size-days_last_year-i:r_size-i]211 losses = help_ES.sort_values()212 expectedloss = np.mean(losses.iloc[0:cutoff])213 ES.append(expectedloss)214 215 data_ES = {}216 data_ES = {"Name": ES}217 ES = pd.DataFrame(ES)218 219 # Drawdown220 Roll_Max = prices.cummax()221 Daily_Drawdown = (prices/Roll_Max - 1.0)222 Max_Daily_Drawdown = Daily_Drawdown.cummin()223 224 Daily_Drawdown = abs(Daily_Drawdown)225 Max_Daily_Drawdown = abs(Max_Daily_Drawdown)226 227 228 #Key Ratios229 key_ratios.columns= ["Name", "ABBN.S", "ADEN.S", "ALCC.S", "CSGN.S", "GEBN.S", "GIVN.S", 230 "LHN.S", "LONN.S", "NESN.S", "NOVN.S", "CFR.S", "ROG.S", "SGSN.S",231 "SIKA.S", "UHR.S", "SLHN.S", "SRENH.S", "SCMN.S", "UBSG.S", "ZURN.S"]232 233 key_ratios_clean = key_ratios["NESN.S"]234 price_earnings_ratio = key_ratios_clean.iloc[4]235 236 # price/book ratio237 price_book_ratio = key_ratios_clean.iloc[5]238 239 # return on equity ratio240 return_on_equity_ratio = key_ratios_clean.iloc[12]241 242 # Dividend yield - indicated annual dividend divided by closing price243 dividend_yield_ratio = key_ratios_clean.iloc[8]244 245 # debt to ratio246 debt_equity_ratio = key_ratios_clean.iloc[20]247 248 249 # =============================================================================250 #Sort all analysis from above to get dataframes which are used on the webpage for the tables and figures251 252 #Overview: Data for Figure Annual Performance253 Data = {'Date': [lastmonth,lastyear, last3years, last5years, last10years],254 'Price': [cum_return_last_month.Price, cum_return_last_year.Price, cum_return_last_3years.Price, cum_return_last_5years.Price, cum_return_last_10years.Price],255 }256 257 self.df_annual_perf = pd.DataFrame(Data, columns = ['Date','Price'])258 259 #Table Price Performance260 Data_Price_Performance = {261 'Name': ["Placeholder"],262 '1 month': [cum_return_last_month.Price],263 '1 Years': [cum_return_last_year.Price],264 '3 Years': [cum_return_last_3years.Price],265 '5 Years': [cum_return_last_5years.Price],266 '10 Years': [cum_return_last_10years.Price],267 'Since Inception': [cum_return_all.Price],268 }269 270 self.df_Price_Performance = pd.DataFrame(Data_Price_Performance, columns = ['1 month','1 Years', '3 Years', '5 Years', '10 Years', 'Since Inception'])271 272 #Overview: Hypothetical Growth 273 V2007 = investment.iloc[r_size-3-days_last_year*12].Price274 V2008 = investment.iloc[r_size-3-days_last_year*11].Price275 V2009 = investment.iloc[r_size-3-days_last_year*10].Price276 V2010 = investment.iloc[r_size-3-days_last_year*9].Price277 V2011 = investment.iloc[r_size-3-days_last_year*8].Price278 V2012 = investment.iloc[r_size-3-days_last_year*7].Price279 V2013 = investment.iloc[r_size-3-days_last_year*6].Price280 V2014 = investment.iloc[r_size-3-days_last_year*5].Price281 V2015 = investment.iloc[r_size-3-days_last_year*4].Price282 V2016 = investment.iloc[r_size-3-days_last_year*3].Price283 V2017 = investment.iloc[r_size-3-days_last_year*2].Price284 V2018 = investment.iloc[r_size-3-days_last_year].Price285 V2019 = investment.iloc[r_size-3].Price286 287 hypothetical_growth = {'Date': ['2006','2007','2008','2009','2010','2011','2012','2013','2014','2015','2016','2017','2018','2019'],288 'Value': [10000,V2007, V2008,V2009,V2010,V2011,V2012,V2013,V2014,V2015,V2016,V2017,V2018,V2019]289 }290 291 self.df_hypothetical_growth = pd.DataFrame(hypothetical_growth, columns = ['Date','Value'])292 293 #Overview: Figure Average Annual Performance294 annual_perf_average = {'Date': [lastmonth,lastyear, last3years, last5years, last10years],295 'Price': [average_return_last_month.Price*252, average_return_last_year.Price*252, average_return_last_3years.Price*252, average_return_last_5years.Price*252, average_return_last_10years.Price*252],296 }297 self.df_annual_perf_average = pd.DataFrame(annual_perf_average, columns = ['Date','Price'])298 299 300 #Overview: igure Risk Potential301 #Define quantiles for Graph302 q0=-1.854444201294828303 q1=-0.8269888130616426304 q2=0.22536003249425604305 q3=0.6619326773878177306 q4=1.1356494832642325307 SR = sharpe_ratio_last_year.Price * math.sqrt(252)308 309 #Define Values for Figure310 if (SR< q1):311 self.SR_q = 0.09312 elif (SR >= q1 and SR< q2):313 self.SR_q = 0.29314 elif (SR >= q2 and SR < q3):315 self.SR_q = 0.49316 elif (SR >= q3 and SR < q4):317 self.SR_q = 0.69318 elif (SR >= q4):319 self.SR_q = 0.89320 321 322 Data_Current_Statistic = {"Name": ["Price change yesterday", "Average Annual Return", "Average daily Volatility","1 Year Volatilty", "1 Year Sharpe Ratio"],323 "Numbers": [round(float(price_change_yesterday.values[0]),2), round(average_return_all.Price*252* 100, 2).astype(str) + '%', round(vola_last_year.Price,3), round(vola_last_year.Price * math.sqrt(252),3), round(sharpe_ratio_last_year.Price * math.sqrt(252),3)]324 }325 326 self.df_Current_Statistic = pd.DataFrame(Data_Current_Statistic, columns = ["Name", "Numbers"])327 328 329 #Price Performance: Table Historic Prices330 Avg_price = pd.DataFrame.mean(prices)331 Data_Historic_Prices = {"Name": ["Current Price", "Price Last Year", "Average Price","Year High", "Year Low"],332 "Numbers": [prices_today.Price.iloc[0],prices_last_year.Price.iloc[0], Avg_price.Price, year_high.Price, year_low.Price]333 }334 335 self.df_Historic_Prices = pd.DataFrame(Data_Historic_Prices, columns = ["Name", "Numbers"]) 336 self.df_Historic_Prices.Numbers = round(self.df_Historic_Prices.Numbers , 2)337 338 339 #Price Performance: Figure Price Development340 date_data_clean = pd.DataFrame(date_data)341 date_data_clean["Prices"] = prices342 343 self.df_Performance_Graph = date_data_clean344 345 #Price Performance: Figure returns 346 date_data_clean2 = pd.DataFrame(date_data)347 date_data_clean2["Returns"] = returns348 349 self.df_Return_Graph = date_data_clean2350 351 352 #Price Performance: Key Ratios353 Data_Key_Ratios = {"Name": ["Price Earnings Ratio", "Price Book Ratio", "Return on Equity","Debt Equity Ratio", "Dividend Yield Ratio",],354 "Numbers": [round(price_earnings_ratio,2), round(price_book_ratio,2), round(return_on_equity_ratio,2), round(debt_equity_ratio* 100, 2).astype(str) + '%', round(dividend_yield_ratio* 100, 2).astype(str) + '%']355 }356 self.df_Key_Ratios = pd.DataFrame(Data_Key_Ratios, columns = ["Name", "Numbers"])357 358 #Risk Measures: Table 1359 Data_Risk_Measures1 = {"Name": [ "Sharpe Ratio last year", "Sharpe Ratio Total", "Daily Drawdown", "Max Daily Drawdown"],360 "Numbers": [round(sharpe_ratio_last_year.Price * math.sqrt(252),2), round(sharpe_ratio_all.Price *math.sqrt(r_size),2), round(Daily_Drawdown.Price.iloc[-1]* 100, 2).astype(str) + '%', round(Max_Daily_Drawdown.Price.iloc[-1]* 100, 2).astype(str) + '%']361 }362 self.df_Risk_Measure1 = pd.DataFrame(Data_Risk_Measures1, columns = ["Name", "Numbers"])363 364 #Risk Measures: Table 2365 Data_Risk_Measures2 = {"Name": [ "Historic Value at Risk","Simulated Value at Risk", "Parametic Value at Risk","Expected Shortfall"],366 "Numbers": [ round(float(HistVar.values[0]),4), round(SimVar,4), round(Covar_Var.Price,4) , round(float(ES.values[0]),4)]367 }368 self.df_Risk_Measure2 = pd.DataFrame(Data_Risk_Measures2, columns = ["Name", "Numbers"])369 370 371 #Risk Measures: Value at Risk 372 data_VaR = pd.DataFrame(df_HistVar, columns = ["Name"])373 data_VaR = data_VaR[::-1].reset_index()374 data_VaR = data_VaR.drop(['index'], axis=1)375 Date_VaR = pd.DataFrame(date_data.iloc[days_last_year:r_size]).reset_index()376 Date_VaR = Date_VaR.drop(['index'], axis=1)377 Date_VaR["Price"] = data_VaR378 self.df_VaR = Date_VaR379 380 #Risk Measures: Expected Shortfall381 Data_ES = pd.DataFrame(data_ES, columns = ["Name"])382 Data_ES = Data_ES[::-1].reset_index()383 Data_ES = Data_ES.drop(['index'], axis=1)384 Date_ES = pd.DataFrame(date_data.iloc[days_last_year:r_size]).reset_index()385 Date_ES = Date_ES.drop(['index'], axis=1)386 Date_ES["Price"] = Data_ES387 self.df_ES = Date_ES388 389 390 #Risk Measures: Drawdown391 date_data_clean1 = pd.DataFrame(date_data)392 date_data_clean1["Max_DD"] = Max_Daily_Drawdown393 date_data_clean1["DD"] = Daily_Drawdown394 date_data_clean1["Roll_Max"] = Roll_Max395 self.df_Max_Daily_Drawdown = date_data_clean1396 397 398 #Technical399 b = prices.Price400 bollinger_mavg = ta.bollinger_mavg(b)401 bollinger_hband = ta.bollinger_hband(b)402 bollinger_lband = ta.bollinger_lband(b)403 bollinger_hband_indicator = ta.bollinger_hband_indicator(b)404 bollinger_lband_indicator=ta.bollinger_lband_indicator(b)405 406 rsi = ta.rsi(b)407 aroon_up = ta.aroon_up(b)408 aroon_down = ta.aroon_down(b)409 410 #Technical Analysis: Table Technical Analysis411 aroon_up_today = aroon_up.values[r_size-2]412 aroon_down_today = aroon_down.values[r_size-2]413 if (aroon_up_today > aroon_down_today):414 if (aroon_up_today > 50):415 aroon_text = 'The Aroon Indicator detects a current strong upwards trend'416 else:417 aroon_text = 'The Aroon Indicator detects a current weak upwards trend'418 else:419 if (aroon_down_today > 50):420 aroon_text = 'The Aroon Indicator detects a current strong downwards trend'421 else:422 aroon_text = 'The Aroon Indicator detects a current weak downwards trend'423 424 425 rsi_today = rsi.values[r_size-2]426 if (rsi_today > 70):427 rsi_text = 'The Relative Strength Index detects a current overvaluation of the stock'428 elif(rsi_today > 30 and rsi_today <70):429 rsi_text = 'The Relative Strength Index detects no current overvaluation or undervaluation of the stock' 430 else:431 rsi_text = 'The Relative Strength Index detects a current undervaluation of the stock'432 433 bollinger_hband_indicator_today = bollinger_hband_indicator.values[r_size-2]434 bollinger_lband_indicator_today = bollinger_lband_indicator.values[r_size-2]435 if (bollinger_hband_indicator_today > bollinger_lband_indicator_today):436 bollinger_text = 'The Bollinger Band Oscillator detects that the current price is higher than the higher Bollinger Band and therefore recommends a buy of the stock'437 elif(bollinger_lband_indicator_today > 0):438 bollinger_text = 'The Bollinger Band Oscillator detects that the current price is lower than the lower Bollinger Band and therefore recommends a selling of the stock'439 else:440 bollinger_text = 'The Bollinger Band Oscillator detects that the current price is between the lower and higher Bollinger Band and therefore recommends no trading activities in the stock'441 TechnicalAnalysis = {"Name": [ "Boolinger Band:", "Relative Strength Index:", "Aroon Indicator:"],442 "Implications": [bollinger_text, rsi_text, aroon_text]443 }444 self.df_TechnicalAnalysis= pd.DataFrame(TechnicalAnalysis, columns = ["Name", "Implications"])445 446 #Technical Analyis: Figure Bollinger447 Date_Bollinger = pd.DataFrame(date_data)448 Date_Bollinger["mavg"] = bollinger_mavg449 Date_Bollinger["hband"] = bollinger_hband450 Date_Bollinger["lband"] = bollinger_lband451 self.df_BollingerBands = Date_Bollinger452 453 #Technical Analyis: Figure RSI454 Date_RSI = pd.DataFrame(date_data)455 Date_RSI["RSI"] = rsi456 457 df_RSI = Date_RSI.drop(Date_RSI.index[0:14]).reset_index()458 self.df_RSI = df_RSI.drop(['index'], axis=1)459 460 #Technical Analyis: Figure Aroon461 Date_aroon = pd.DataFrame(date_data)462 Date_aroon["aroon_up"] = aroon_up463 Date_aroon["aroon_down"] = aroon_down464 self.df_AroonIndicator = Date_aroon...
0003_auto_20170616_1404.py
Source:0003_auto_20170616_1404.py
1# -*- coding: utf-8 -*-2# Generated by Django 1.11.1 on 2017-06-16 14:043from __future__ import unicode_literals4from django.db import migrations, models5class Migration(migrations.Migration):6 dependencies = [7 ('raw', '0002_viewingtimes'),8 ]9 operations = [10 migrations.AddField(11 model_name='address',12 name='last_updated',13 field=models.DateTimeField(auto_now=True),14 ),15 migrations.AddField(16 model_name='agency',17 name='last_updated',18 field=models.DateTimeField(auto_now=True),19 ),20 migrations.AddField(21 model_name='agent',22 name='last_updated',23 field=models.DateTimeField(auto_now=True),24 ),25 migrations.AddField(26 model_name='attribute',27 name='last_updated',28 field=models.DateTimeField(auto_now=True),29 ),30 migrations.AddField(31 model_name='attributeoption',32 name='last_updated',33 field=models.DateTimeField(auto_now=True),34 ),35 migrations.AddField(36 model_name='attributerange',37 name='last_updated',38 field=models.DateTimeField(auto_now=True),39 ),40 migrations.AddField(41 model_name='bid',42 name='last_updated',43 field=models.DateTimeField(auto_now=True),44 ),45 migrations.AddField(46 model_name='bidcollection',47 name='last_updated',48 field=models.DateTimeField(auto_now=True),49 ),50 migrations.AddField(51 model_name='branding',52 name='last_updated',53 field=models.DateTimeField(auto_now=True),54 ),55 migrations.AddField(56 model_name='broadbandtechnology',57 name='last_updated',58 field=models.DateTimeField(auto_now=True),59 ),60 migrations.AddField(61 model_name='category',62 name='last_updated',63 field=models.DateTimeField(auto_now=True),64 ),65 migrations.AddField(66 model_name='charity',67 name='last_updated',68 field=models.DateTimeField(auto_now=True),69 ),70 migrations.AddField(71 model_name='contactdetails',72 name='last_updated',73 field=models.DateTimeField(auto_now=True),74 ),75 migrations.AddField(76 model_name='currentshippingpromotion',77 name='last_updated',78 field=models.DateTimeField(auto_now=True),79 ),80 migrations.AddField(81 model_name='dealer',82 name='last_updated',83 field=models.DateTimeField(auto_now=True),84 ),85 migrations.AddField(86 model_name='dealership',87 name='last_updated',88 field=models.DateTimeField(auto_now=True),89 ),90 migrations.AddField(91 model_name='dealershiplistingcounts',92 name='last_updated',93 field=models.DateTimeField(auto_now=True),94 ),95 migrations.AddField(96 model_name='dealershipphonenumbers',97 name='last_updated',98 field=models.DateTimeField(auto_now=True),99 ),100 migrations.AddField(101 model_name='dealershowroom',102 name='last_updated',103 field=models.DateTimeField(auto_now=True),104 ),105 migrations.AddField(106 model_name='district',107 name='last_updated',108 field=models.DateTimeField(auto_now=True),109 ),110 migrations.AddField(111 model_name='embeddedcontent',112 name='last_updated',113 field=models.DateTimeField(auto_now=True),114 ),115 migrations.AddField(116 model_name='fixedpriceofferdetails',117 name='last_updated',118 field=models.DateTimeField(auto_now=True),119 ),120 migrations.AddField(121 model_name='fixedpriceofferrecipient',122 name='last_updated',123 field=models.DateTimeField(auto_now=True),124 ),125 migrations.AddField(126 model_name='flatmate',127 name='last_updated',128 field=models.DateTimeField(auto_now=True),129 ),130 migrations.AddField(131 model_name='flatmateadjacentsuburbids',132 name='last_updated',133 field=models.DateTimeField(auto_now=True),134 ),135 migrations.AddField(136 model_name='flatmateadjacentsuburbnames',137 name='last_updated',138 field=models.DateTimeField(auto_now=True),139 ),140 migrations.AddField(141 model_name='flatmatephotourls',142 name='last_updated',143 field=models.DateTimeField(auto_now=True),144 ),145 migrations.AddField(146 model_name='flatmates',147 name='last_updated',148 field=models.DateTimeField(auto_now=True),149 ),150 migrations.AddField(151 model_name='foundcategory',152 name='last_updated',153 field=models.DateTimeField(auto_now=True),154 ),155 migrations.AddField(156 model_name='geographiclocation',157 name='last_updated',158 field=models.DateTimeField(auto_now=True),159 ),160 migrations.AddField(161 model_name='largebannerimage',162 name='last_updated',163 field=models.DateTimeField(auto_now=True),164 ),165 migrations.AddField(166 model_name='listeditemdetail',167 name='last_updated',168 field=models.DateTimeField(auto_now=True),169 ),170 migrations.AddField(171 model_name='locality',172 name='last_updated',173 field=models.DateTimeField(auto_now=True),174 ),175 migrations.AddField(176 model_name='member',177 name='last_updated',178 field=models.DateTimeField(auto_now=True),179 ),180 migrations.AddField(181 model_name='memberprofile',182 name='last_updated',183 field=models.DateTimeField(auto_now=True),184 ),185 migrations.AddField(186 model_name='memberrequestinformation',187 name='last_updated',188 field=models.DateTimeField(auto_now=True),189 ),190 migrations.AddField(191 model_name='membershipdistrict',192 name='last_updated',193 field=models.DateTimeField(auto_now=True),194 ),195 migrations.AddField(196 model_name='membershiplocality',197 name='last_updated',198 field=models.DateTimeField(auto_now=True),199 ),200 migrations.AddField(201 model_name='motorwebbasicreport',202 name='last_updated',203 field=models.DateTimeField(auto_now=True),204 ),205 migrations.AddField(206 model_name='openhome',207 name='last_updated',208 field=models.DateTimeField(auto_now=True),209 ),210 migrations.AddField(211 model_name='option',212 name='last_updated',213 field=models.DateTimeField(auto_now=True),214 ),215 migrations.AddField(216 model_name='optionset',217 name='last_updated',218 field=models.DateTimeField(auto_now=True),219 ),220 migrations.AddField(221 model_name='optionsetvalues',222 name='last_updated',223 field=models.DateTimeField(auto_now=True),224 ),225 migrations.AddField(226 model_name='photo',227 name='last_updated',228 field=models.DateTimeField(auto_now=True),229 ),230 migrations.AddField(231 model_name='photourl',232 name='last_updated',233 field=models.DateTimeField(auto_now=True),234 ),235 migrations.AddField(236 model_name='properties',237 name='last_updated',238 field=models.DateTimeField(auto_now=True),239 ),240 migrations.AddField(241 model_name='property',242 name='last_updated',243 field=models.DateTimeField(auto_now=True),244 ),245 migrations.AddField(246 model_name='propertyadjacentsuburbids',247 name='last_updated',248 field=models.DateTimeField(auto_now=True),249 ),250 migrations.AddField(251 model_name='propertyadjacentsuburbnames',252 name='last_updated',253 field=models.DateTimeField(auto_now=True),254 ),255 migrations.AddField(256 model_name='propertyphotourls',257 name='last_updated',258 field=models.DateTimeField(auto_now=True),259 ),260 migrations.AddField(261 model_name='question',262 name='last_updated',263 field=models.DateTimeField(auto_now=True),264 ),265 migrations.AddField(266 model_name='questions',267 name='last_updated',268 field=models.DateTimeField(auto_now=True),269 ),270 migrations.AddField(271 model_name='refunddetails',272 name='last_updated',273 field=models.DateTimeField(auto_now=True),274 ),275 migrations.AddField(276 model_name='sale',277 name='last_updated',278 field=models.DateTimeField(auto_now=True),279 ),280 migrations.AddField(281 model_name='searchparameter',282 name='last_updated',283 field=models.DateTimeField(auto_now=True),284 ),285 migrations.AddField(286 model_name='shippingoption',287 name='last_updated',288 field=models.DateTimeField(auto_now=True),289 ),290 migrations.AddField(291 model_name='simplememberprofile',292 name='last_updated',293 field=models.DateTimeField(auto_now=True),294 ),295 migrations.AddField(296 model_name='sponsorlink',297 name='last_updated',298 field=models.DateTimeField(auto_now=True),299 ),300 migrations.AddField(301 model_name='store',302 name='last_updated',303 field=models.DateTimeField(auto_now=True),304 ),305 migrations.AddField(306 model_name='storepromotion',307 name='last_updated',308 field=models.DateTimeField(auto_now=True),309 ),310 migrations.AddField(311 model_name='suburb',312 name='last_updated',313 field=models.DateTimeField(auto_now=True),314 ),315 migrations.AddField(316 model_name='suburbadjacentsuburbs',317 name='last_updated',318 field=models.DateTimeField(auto_now=True),319 ),320 migrations.AddField(321 model_name='variant',322 name='last_updated',323 field=models.DateTimeField(auto_now=True),324 ),325 migrations.AddField(326 model_name='variantdefinition',327 name='last_updated',328 field=models.DateTimeField(auto_now=True),329 ),330 migrations.AddField(331 model_name='variantdefinitionsummary',332 name='last_updated',333 field=models.DateTimeField(auto_now=True),334 ),335 migrations.AddField(336 model_name='viewingtime',337 name='last_updated',338 field=models.DateTimeField(auto_now=True),339 ),340 migrations.AddField(341 model_name='viewingtimes',342 name='last_updated',343 field=models.DateTimeField(auto_now=True),344 ),...
expressions.py
Source:expressions.py
1"""This module contains the expressions applicable for CronTrigger's fields."""2from calendar import monthrange3import re4from apscheduler.util import asint5__all__ = ('AllExpression', 'RangeExpression', 'WeekdayRangeExpression',6 'WeekdayPositionExpression', 'LastDayOfMonthExpression')7WEEKDAYS = ['mon', 'tue', 'wed', 'thu', 'fri', 'sat', 'sun']8MONTHS = ['jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct', 'nov', 'dec']9class AllExpression(object):10 value_re = re.compile(r'\*(?:/(?P<step>\d+))?$')11 def __init__(self, step=None):12 self.step = asint(step)13 if self.step == 0:14 raise ValueError('Increment must be higher than 0')15 def validate_range(self, field_name):16 from apscheduler.triggers.cron.fields import MIN_VALUES, MAX_VALUES17 value_range = MAX_VALUES[field_name] - MIN_VALUES[field_name]18 if self.step and self.step > value_range:19 raise ValueError('the step value ({}) is higher than the total range of the '20 'expression ({})'.format(self.step, value_range))21 def get_next_value(self, date, field):22 start = field.get_value(date)23 minval = field.get_min(date)24 maxval = field.get_max(date)25 start = max(start, minval)26 if not self.step:27 next = start28 else:29 distance_to_next = (self.step - (start - minval)) % self.step30 next = start + distance_to_next31 if next <= maxval:32 return next33 def __eq__(self, other):34 return isinstance(other, self.__class__) and self.step == other.step35 def __str__(self):36 if self.step:37 return '*/%d' % self.step38 return '*'39 def __repr__(self):40 return "%s(%s)" % (self.__class__.__name__, self.step)41class RangeExpression(AllExpression):42 value_re = re.compile(43 r'(?P<first>\d+)(?:-(?P<last>\d+))?(?:/(?P<step>\d+))?$')44 def __init__(self, first, last=None, step=None):45 super(RangeExpression, self).__init__(step)46 first = asint(first)47 last = asint(last)48 if last is None and step is None:49 last = first50 if last is not None and first > last:51 raise ValueError('The minimum value in a range must not be higher than the maximum')52 self.first = first53 self.last = last54 def validate_range(self, field_name):55 from apscheduler.triggers.cron.fields import MIN_VALUES, MAX_VALUES56 super(RangeExpression, self).validate_range(field_name)57 if self.first < MIN_VALUES[field_name]:58 raise ValueError('the first value ({}) is lower than the minimum value ({})'59 .format(self.first, MIN_VALUES[field_name]))60 if self.last is not None and self.last > MAX_VALUES[field_name]:61 raise ValueError('the last value ({}) is higher than the maximum value ({})'62 .format(self.last, MAX_VALUES[field_name]))63 value_range = (self.last or MAX_VALUES[field_name]) - self.first64 if self.step and self.step > value_range:65 raise ValueError('the step value ({}) is higher than the total range of the '66 'expression ({})'.format(self.step, value_range))67 def get_next_value(self, date, field):68 startval = field.get_value(date)69 minval = field.get_min(date)70 maxval = field.get_max(date)71 # Apply range limits72 minval = max(minval, self.first)73 maxval = min(maxval, self.last) if self.last is not None else maxval74 nextval = max(minval, startval)75 # Apply the step if defined76 if self.step:77 distance_to_next = (self.step - (nextval - minval)) % self.step78 nextval += distance_to_next79 return nextval if nextval <= maxval else None80 def __eq__(self, other):81 return (isinstance(other, self.__class__) and self.first == other.first and82 self.last == other.last)83 def __str__(self):84 if self.last != self.first and self.last is not None:85 range = '%d-%d' % (self.first, self.last)86 else:87 range = str(self.first)88 if self.step:89 return '%s/%d' % (range, self.step)90 return range91 def __repr__(self):92 args = [str(self.first)]93 if self.last != self.first and self.last is not None or self.step:94 args.append(str(self.last))95 if self.step:96 args.append(str(self.step))97 return "%s(%s)" % (self.__class__.__name__, ', '.join(args))98class MonthRangeExpression(RangeExpression):99 value_re = re.compile(r'(?P<first>[a-z]+)(?:-(?P<last>[a-z]+))?', re.IGNORECASE)100 def __init__(self, first, last=None):101 try:102 first_num = MONTHS.index(first.lower()) + 1103 except ValueError:104 raise ValueError('Invalid month name "%s"' % first)105 if last:106 try:107 last_num = MONTHS.index(last.lower()) + 1108 except ValueError:109 raise ValueError('Invalid month name "%s"' % last)110 else:111 last_num = None112 super(MonthRangeExpression, self).__init__(first_num, last_num)113 def __str__(self):114 if self.last != self.first and self.last is not None:115 return '%s-%s' % (MONTHS[self.first - 1], MONTHS[self.last - 1])116 return MONTHS[self.first - 1]117 def __repr__(self):118 args = ["'%s'" % MONTHS[self.first]]119 if self.last != self.first and self.last is not None:120 args.append("'%s'" % MONTHS[self.last - 1])121 return "%s(%s)" % (self.__class__.__name__, ', '.join(args))122class WeekdayRangeExpression(RangeExpression):123 value_re = re.compile(r'(?P<first>[a-z]+)(?:-(?P<last>[a-z]+))?', re.IGNORECASE)124 def __init__(self, first, last=None):125 try:126 first_num = WEEKDAYS.index(first.lower())127 except ValueError:128 raise ValueError('Invalid weekday name "%s"' % first)129 if last:130 try:131 last_num = WEEKDAYS.index(last.lower())132 except ValueError:133 raise ValueError('Invalid weekday name "%s"' % last)134 else:135 last_num = None136 super(WeekdayRangeExpression, self).__init__(first_num, last_num)137 def __str__(self):138 if self.last != self.first and self.last is not None:139 return '%s-%s' % (WEEKDAYS[self.first], WEEKDAYS[self.last])140 return WEEKDAYS[self.first]141 def __repr__(self):142 args = ["'%s'" % WEEKDAYS[self.first]]143 if self.last != self.first and self.last is not None:144 args.append("'%s'" % WEEKDAYS[self.last])145 return "%s(%s)" % (self.__class__.__name__, ', '.join(args))146class WeekdayPositionExpression(AllExpression):147 options = ['1st', '2nd', '3rd', '4th', '5th', 'last']148 value_re = re.compile(r'(?P<option_name>%s) +(?P<weekday_name>(?:\d+|\w+))' %149 '|'.join(options), re.IGNORECASE)150 def __init__(self, option_name, weekday_name):151 super(WeekdayPositionExpression, self).__init__(None)152 try:153 self.option_num = self.options.index(option_name.lower())154 except ValueError:155 raise ValueError('Invalid weekday position "%s"' % option_name)156 try:157 self.weekday = WEEKDAYS.index(weekday_name.lower())158 except ValueError:159 raise ValueError('Invalid weekday name "%s"' % weekday_name)160 def get_next_value(self, date, field):161 # Figure out the weekday of the month's first day and the number of days in that month162 first_day_wday, last_day = monthrange(date.year, date.month)163 # Calculate which day of the month is the first of the target weekdays164 first_hit_day = self.weekday - first_day_wday + 1165 if first_hit_day <= 0:166 first_hit_day += 7167 # Calculate what day of the month the target weekday would be168 if self.option_num < 5:169 target_day = first_hit_day + self.option_num * 7170 else:171 target_day = first_hit_day + ((last_day - first_hit_day) // 7) * 7172 if target_day <= last_day and target_day >= date.day:173 return target_day174 def __eq__(self, other):175 return (super(WeekdayPositionExpression, self).__eq__(other) and176 self.option_num == other.option_num and self.weekday == other.weekday)177 def __str__(self):178 return '%s %s' % (self.options[self.option_num], WEEKDAYS[self.weekday])179 def __repr__(self):180 return "%s('%s', '%s')" % (self.__class__.__name__, self.options[self.option_num],181 WEEKDAYS[self.weekday])182class LastDayOfMonthExpression(AllExpression):183 value_re = re.compile(r'last', re.IGNORECASE)184 def __init__(self):185 super(LastDayOfMonthExpression, self).__init__(None)186 def get_next_value(self, date, field):187 return monthrange(date.year, date.month)[1]188 def __str__(self):189 return 'last'190 def __repr__(self):...
single_strategy.py
Source:single_strategy.py
1# encoding: utf-82# author: gao-ming3# time: 2019/7/14--22:024# desc:5def stragtegy_MACD(df):6 macd_df = df[['DIF', 'DEA', 'MACD']]7 macd_list = df['MACD']8 # var_macd_list=[]9 # i=110 # while 1:11 # var_macd=macd_list[-i]-macd_list[-1-1]12 # var_macd_list.append(var_macd)13 last_macd = macd_df.iloc[-1, :]14 last_DIF = last_macd['DIF']15 last_DEA = last_macd['DEA']16 last_MACD = last_macd['MACD']17 up_trend = False18 if last_DIF > last_DEA and last_MACD > 0:19 up_trend = 'ä¸åè¶å¿'20 else:21 up_trend = 'éè¡æä¸éè¶å¿'22 var_macd_mark = []23 for i in range(1, len(macd_list)):24 var_macd = macd_list[-i] - macd_list[-i - 1]25 if i == 1:26 try:27 first_mark = var_macd / abs(var_macd)28 except:29 first_mark = 030 if var_macd > 0:31 var_macd_mark.append(1)32 elif var_macd < 0:33 var_macd_mark.append(-1)34 else:35 var_macd_mark.append(0)36 i += 137 if first_mark == 1:38 trend_add = 'å¢å¼º'39 else:40 trend_add = 'åå¼±'41 res = {42 'trend': up_trend + '--' + trend_add,43 }44 return res45def strategy_BOLL(df, n: int = 20):46 """47 å¤æä»·æ ¼è¾¹ç ä»·æ ¼è¿å¨ æä¸å®çè¾¹ç48 :param df:49 :param n: 使ç¨çæ°æ®é¿åº¦ é»è®¤2050 :return: ä¸ä¸è½¨ä»·æ ¼ 以å å¨ä¸è½¨æä¸è½¨éè¿ï¼ç»åºæä½çä»·æ ¼åºé´ å
¶å®ä¸º051 """52 boll_df = df.iloc[-n:, :]53 mid_s = boll_df['boll_mid']54 last_mid = mid_s[-1]55 # 计ç®ä¸ä¸å¨æçboll轨é56 next_mid = mid_s[-1] * 2 - mid_s[-2]57 next_up = boll_df['boll_up'][-1] * 2 - boll_df['boll_up'][-2]58 next_dn = boll_df['boll_dn'][-1] * 2 - boll_df['boll_dn'][-2]59 last_close = df['close'][-1]60 risk_rate_income, section = 0, 061 # boll线ï¼ä¸è½¨éè¿ï¼ååº ç»åºä»·æ ¼åºé´62 if abs(last_close - next_up) < last_mid * 0.025:63 section = (next_up - 0.025 * last_mid, next_up + 0.025 * last_mid)64 # ååº æ¶çé£é©æ¯å¾å°65 risk_rate_income = 0.0166 # boll线ä¸è½¨ ä¹°å
¥ ç»åºä¹°å
¥åºé´67 if abs(last_close - next_dn) < last_mid * 0.025:68 section = (next_dn - 0.025 * last_mid, next_dn + 0.025 * last_mid)69 try:70 risk_rate_income = (next_mid - last_close) / (last_close - next_dn) - 171 except:72 risk_rate_income = 673 if risk_rate_income > 5 or risk_rate_income < -1:74 risk_rate_income = 575 res = {76 'next_up': next_up,77 'next_dn': next_dn,78 # é£é©åæ¥æ¯79 'risk_income': '%.2f' % risk_rate_income,80 # ä»·æ ¼åèåºé´81 'section': section,82 }83 return res84def strategy_KDJ(df):85 """86 å¤ææ¦çè¾¹ç è¶
ä¹° è·çæ¦ç大 è¶
å 涨çæ¦ç大87 :param df:88 :return:89 """90 kdj_df = df[['kdj_K', 'kdj_D']]91 kdj_df['K-D'] = kdj_df['kdj_K'] - kdj_df['kdj_D']92 last_kdj = kdj_df.iloc[-1, :]93 kdj_K = last_kdj['kdj_K']94 kdj_D = last_kdj['kdj_D']95 too_much = False96 if kdj_K > 80 or kdj_D > 80:97 too_much = 'è¿å
¥è¶
ä¹°åºé´'98 if kdj_K < 20 or kdj_D < 20:99 too_much = 'è¿å
¥è¶
ååºé´'100 res = {101 'kdj_K': kdj_K,102 'kdj_D': kdj_D,103 }104 if too_much:105 res['kdj_res'] = too_much106 return res107def strategy_RSI(df):108 rsi_df = df[['RSI_6', 'RSI_12', 'RSI_24']]109 pass110def strategy_MA(df, n: int = 20):111 """112 å线çç¥ï¼å¤æè¶å¿113 :param df:114 :return:115 """116 try:117 ma_df = df[f'close_MA_{n}']118 except Exception as e:119 raise Exception('å线å¨æææ°æ®æ误ï¼')120 # ä¸åè¶å¿ï¼å¹³çï¼ä¸éè¶å¿ï¼æåè¿åçè¶å¿ç»æ121 # mark_up,mark_line,mark_dn,trend=0,0,0,0122 # print('0',mark_up)123 # bollä¸çº¿çæ³¢å¨å¼è¾¹çï¼å¨è¿ä¸ªå¼å
æ³¢å¨ï¼è®¤ä¸ºæ¯åççæ³¢å¨124 stand_value = ma_df[-1] * 0.007125 var_mid_list = []126 var_mid_mark = []127 for i in range(2, n):128 # 计ç®bollä¸çº¿ååå·®å¼129 var_mid = ma_df[-i] - ma_df[1 - i]130 var_mid_list.append(var_mid)131 # å¤æè¶å¿132 if abs(var_mid) < stand_value:133 mark = 0 # å¹³ç134 elif var_mid > stand_value:135 mark = 1 # ä¸åè¶å¿136 else:137 mark = -1 # ä¸éè¶å¿138 var_mid_mark.append(mark)139 last_mark = var_mid_mark[0]140 # ä¿åè¶å¿ï¼ä»¥åå 强è¿æ¯åå¼±141 trend_res = [last_mark, -1]142 if var_mid_mark[0] * var_mid_mark[1] > 0:143 if abs(var_mid_list[0]) > abs(var_mid_list[1]):144 trend_res[1] = 1 # è¶å¿å 强145 # å¤æè¶å¿å»¶ç»çå¨æ146 trend_num = 0147 for i in range(1, n):148 trend_num += 1149 if var_mid_list[i] * var_mid_list[i - 1] < 0:150 break151 suggest = 'æ¸
ä»' if last_mark == -1 else 'æä»æ波段'152 trend_dict = {153 '1': 'ä¸å',154 '0': 'å¹³ç',155 '-1': 'ä¸é',156 }157 trend_add = {158 '-1': 'åå¼±',159 '1': 'å 强',160 }161 trend_judge = trend_dict[trend_res[0]] + ' ' + trend_add[trend_res[1]]162 res = {163 'suggest': suggest,164 # è¶å¿165 'trend': trend_judge,166 # å¨ææç»çé¿åº¦167 'trend_num': trend_num,168 }169 return res170def strategy_VOL(df):171 """172 æ ¹æ®æ交éæ¥å¤æ173 :param df:å«ææ交é åæ交éåå¼çdataframe174 :return: å¤æç»æ:175 'abnormal':éè½æ¯å¦å¼å¸¸,176 'vol_status':éè½å¢åç¶æ,177 'period':æç»æ¶é´,178 """179 vol_df = df[['vol', 'vol_MA_10']]180 # å¤æéè½å¼å¸¸181 vol_abnormal = 0182 last_vol = vol_df[-1, :]183 if last_vol['vol'] / last_vol['vol_MA_10'] > 2:184 vol_abnormal = 'éè½å¼å¸¸'185 # è®°å½æ交éçå¢åç¶æ186 var_vol_MA_sign = []187 for i in range(1, 50):188 last_vol = vol_df[-i, :]189 if last_vol['vol'] - last_vol['vol_MA_10'] >= 0:190 var_vol_MA_sign.append(1)191 else:192 var_vol_MA_sign.append(-1)193 if var_vol_MA_sign[0] == 1:194 vol_status = 'éè½å¢å '195 else:196 vol_status = 'éè½åå°'197 # ä¿åååçèç¹index198 change_index = []199 #200 for i in range(len(var_vol_MA_sign)):201 if var_vol_MA_sign[i] * var_vol_MA_sign[i + 1] < 0:202 change_index.append(i)203 # éè½äº¤æ¿ä¸è½è¶
è¿3个å¨æï¼å¦åæç»ç¶æç»æ204 i = 0205 while 1:206 if change_index[i + 1] - change_index[i] > 3:207 vol_period = i208 break209 i += 2210 vol_period = change_index[vol_period]211 res = {212 'abnormal': vol_abnormal,213 'vol_status': vol_status,214 'period': vol_period,215 }...
Playwright tutorial
LambdaTest’s Playwright tutorial will give you a broader idea about the Playwright automation framework, its unique features, and use cases with examples to exceed your understanding of Playwright testing. This tutorial will give A to Z guidance, from installing the Playwright framework to some best practices and advanced concepts.
Chapters:
- What is Playwright : Playwright is comparatively new but has gained good popularity. Get to know some history of the Playwright with some interesting facts connected with it.
- How To Install Playwright : Learn in detail about what basic configuration and dependencies are required for installing Playwright and run a test. Get a step-by-step direction for installing the Playwright automation framework.
- Playwright Futuristic Features: Launched in 2020, Playwright gained huge popularity quickly because of some obliging features such as Playwright Test Generator and Inspector, Playwright Reporter, Playwright auto-waiting mechanism and etc. Read up on those features to master Playwright testing.
- What is Component Testing: Component testing in Playwright is a unique feature that allows a tester to test a single component of a web application without integrating them with other elements. Learn how to perform Component testing on the Playwright automation framework.
- Inputs And Buttons In Playwright: Every website has Input boxes and buttons; learn about testing inputs and buttons with different scenarios and examples.
- Functions and Selectors in Playwright: Learn how to launch the Chromium browser with Playwright. Also, gain a better understanding of some important functions like “BrowserContext,” which allows you to run multiple browser sessions, and “newPage” which interacts with a page.
- Handling Alerts and Dropdowns in Playwright : Playwright interact with different types of alerts and pop-ups, such as simple, confirmation, and prompt, and different types of dropdowns, such as single selector and multi-selector get your hands-on with handling alerts and dropdown in Playright testing.
- Playwright vs Puppeteer: Get to know about the difference between two testing frameworks and how they are different than one another, which browsers they support, and what features they provide.
- Run Playwright Tests on LambdaTest: Playwright testing with LambdaTest leverages test performance to the utmost. You can run multiple Playwright tests in Parallel with the LammbdaTest test cloud. Get a step-by-step guide to run your Playwright test on the LambdaTest platform.
- Playwright Python Tutorial: Playwright automation framework support all major languages such as Python, JavaScript, TypeScript, .NET and etc. However, there are various advantages to Python end-to-end testing with Playwright because of its versatile utility. Get the hang of Playwright python testing with this chapter.
- Playwright End To End Testing Tutorial: Get your hands on with Playwright end-to-end testing and learn to use some exciting features such as TraceViewer, Debugging, Networking, Component testing, Visual testing, and many more.
- Playwright Video Tutorial: Watch the video tutorials on Playwright testing from experts and get a consecutive in-depth explanation of Playwright automation testing.
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